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» Streaming Algorithms for extent problems in high dimensions
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CCCG
2006
13 years 7 months ago
A Simple Streaming Algorithm for Minimum Enclosing Balls
We analyze an extremely simple approximation algorithm for computing the minimum enclosing ball (or the 1-center) of a set of points in high dimensions. We prove that this algorit...
Hamid Zarrabi-Zadeh, Timothy M. Chan
ESCAPE
2007
Springer
212views Algorithms» more  ESCAPE 2007»
13 years 12 months ago
Streaming Algorithms for Data in Motion
We propose two new data stream models: the reset model and the delta model, motivated by applications to databases, and to tracking the location of spatial points. We present algor...
Michael Hoffmann 0002, S. Muthukrishnan, Rajeev Ra...
ICML
2008
IEEE
14 years 6 months ago
An empirical evaluation of supervised learning in high dimensions
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
Rich Caruana, Nikolaos Karampatziakis, Ainur Yesse...
KDD
2001
ACM
196views Data Mining» more  KDD 2001»
14 years 6 months ago
Efficient discovery of error-tolerant frequent itemsets in high dimensions
We present a generalization of frequent itemsets allowing the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifie...
Cheng Yang, Usama M. Fayyad, Paul S. Bradley
ICDM
2002
IEEE
158views Data Mining» more  ICDM 2002»
13 years 10 months ago
Adaptive dimension reduction for clustering high dimensional data
It is well-known that for high dimensional data clustering, standard algorithms such as EM and the K-means are often trapped in local minimum. Many initialization methods were pro...
Chris H. Q. Ding, Xiaofeng He, Hongyuan Zha, Horst...